Many methods exist which aim to cluster coefficients in the univariate linear model.
For example, [1]}, [2]}, and
[3]} all propose methods which minimize a loss function plus a
penalty term to encourage clustering in the coefficients. These methods are
computationally efficient as they are all a special case of the generalized
lasso [4]}. In the Bayesian setting, the coefficient
clustering problem is solved by utilizing mixture models as priors for the
coefficients. [5]} uses finite mixture models to cluster
the coefficients, while [6]}, [7]},
[8]}, and [9]} all use Dirichlet
Process priors to estimate clusters.
